Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	121,383
2	South Dakota	112,092
3	Wisconsin	89,384
4	Iowa	89,288
5	Nebraska	86,397
6	Utah	86,280
7	Tennessee	83,843
8	Rhode Island	83,020
9	Idaho	79,117
10	Kansas	77,387
11	Wyoming	76,731
12	Montana	76,443
13	Indiana	76,416
14	Illinois	76,219
15	Arkansas	74,603
16	Alabama	73,671
17	Minnesota	73,650
18	Oklahoma	73,524
19	Nevada	73,106
20	Mississippi	72,513
21	Arizona	71,967
22	Missouri	68,261
23	New Mexico	68,149
24	Louisiana	67,818
25	Alaska	63,892
26	Florida	61,612
27	Texas	61,117
28	Georgia	60,661
29	Kentucky	60,171
30	Ohio	59,917
31	South Carolina	59,725
32	Delaware	59,004
33	California	58,404
34	Colorado	58,273
35	Massachusetts	54,432
36	New Jersey	53,971
37	Michigan	52,789
38	Connecticut	52,087
39	North Carolina	51,589
40	Pennsylvania	50,465
41	New York	50,327
42	West Virginia	47,615
43	Maryland	45,790
44	District of Columbia	41,067
45	Virginia	40,956
46	Puerto Rico	34,413
47	Washington	32,970
48	New Hampshire	32,380
49	Oregon	27,011
50	Maine	18,003
51	Hawaii	15,156
52	Vermont	11,878

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	1,056
2	Utah	1,042
3	Tennessee	965
4	Arkansas	951
5	Georgia	921
6	Alabama	906
7	Massachusetts	882
8	Arizona	879
9	Mississippi	864
10	California	833
11	Indiana	752
12	Rhode Island	738
13	Idaho	734
14	Kansas	727
15	West Virginia	725
16	Ohio	722
17	Nevada	704
18	Oklahoma	703
19	Pennsylvania	693
20	New York	691
21	Texas	691
22	Nebraska	677
23	Delaware	673
24	New Mexico	673
25	Florida	669
26	Missouri	638
27	South Carolina	612
28	North Carolina	606
29	Wisconsin	589
30	Kentucky	583
31	New Hampshire	578
32	South Dakota	567
33	Illinois	563
34	Iowa	534
35	Virginia	523
36	Montana	505
37	New Jersey	505
38	Alaska	485
39	Maine	466
40	Colorado	424
41	Connecticut	421
42	North Dakota	409
43	Maryland	407
44	Wyoming	406
45	Washington	329
46	Minnesota	305
47	District of Columbia	302
48	Oregon	286
49	Puerto Rico	274
50	Michigan	263
51	Vermont	155
52	Hawaii	82

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,143
2	New York	1,930
3	Massachusetts	1,802
4	North Dakota	1,704
5	South Dakota	1,682
6	Connecticut	1,681
7	Rhode Island	1,677
8	Louisiana	1,610
9	Mississippi	1,608
10	Illinois	1,418
11	Michigan	1,302
12	Pennsylvania	1,251
13	Iowa	1,233
14	Indiana	1,227
15	Arizona	1,219
16	Arkansas	1,218
17	New Mexico	1,182
18	District of Columbia	1,113
19	South Carolina	1,028
20	Nevada	1,018
21	Florida	1,009
22	Tennessee	1,002
23	Georgia	997
24	Alabama	984
25	Maryland	975
26	Texas	970
27	Missouri	959
28	Minnesota	954
29	Delaware	950
30	Kansas	940
31	Montana	900
32	Wisconsin	900
33	Nebraska	867
34	Colorado	847
35	Idaho	804
36	Ohio	766
37	Wyoming	756
38	West Virginia	746
39	Kentucky	661
40	California	657
41	North Carolina	649
42	Oklahoma	629
43	Virginia	589
44	New Hampshire	558
45	Puerto Rico	470
46	Washington	462
47	Utah	396
48	Oregon	352
49	Alaska	270
50	Maine	258
51	Vermont	217
52	Hawaii	201

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Pennsylvania	23
2	Kansas	22
3	Wyoming	19
4	Arizona	18
5	Indiana	18
6	Mississippi	17
7	Nevada	17
8	Arkansas	15
9	Iowa	15
10	New Mexico	15
11	South Dakota	15
12	Tennessee	15
13	New Jersey	14
14	Illinois	13
15	West Virginia	13
16	California	12
17	Massachusetts	12
18	Missouri	11
19	Nebraska	11
20	Ohio	11
21	Rhode Island	11
22	Colorado	10
23	Idaho	10
24	Louisiana	10
25	Montana	10
26	New Hampshire	10
27	Texas	10
28	Wisconsin	10
29	Delaware	9
30	Minnesota	9
31	North Dakota	9
32	Washington	9
33	Connecticut	8
34	Maryland	8
35	Michigan	8
36	Oklahoma	8
37	Alabama	7
38	District of Columbia	7
39	Georgia	7
40	New York	7
41	South Carolina	7
42	Kentucky	6
43	North Carolina	6
44	Virginia	6
45	Florida	5
46	Maine	5
47	Puerto Rico	4
48	Utah	4
49	Oregon	3
50	Vermont	3
51	Alaska	2
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	271,737	1	99
Norton	Kansas	219,922	2	99
Lincoln	Arkansas	216,523	3	99
Dewey	South Dakota	214,358	4	99
Bon Homme	South Dakota	214,027	5	99
Davidson	Tennessee	99,132	311	90
Richland	South Carolina	63,364	1602	49
York	South Carolina	54,125	2058	34
Orange	California	52,334	2141	31
Pierce	Washington	30,520	2831	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Davidson	Tennessee	821	1815	42
Richland	South Carolina	806	1840	41
York	South Carolina	641	2131	32
Orange	California	590	2216	29
Pierce	Washington	385	2579	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.